Seeing Beyond Vision
“To see beyond vision — robots that imagine what they cannot see.”
Challenge uncertainty, Change perception through latent dynamics, and drive Impact toward uncertainty-aware embodied intelligence.
Deep Stochastic State-Space Models (Deep SSSM)
Deep SSSM pipeline: Observation → Latent State Inference → Stochastic Prediction → Control & Planning → Feedback
Perception → Inference → Prediction → Planning → Action
(Robots learn to act beyond direct observation through stochastic world modeling)
Deep SSSMs are probabilistic world models that combine stochastic latent dynamics with control-theoretic planning. They enable robots to reason about hidden or occluded states, predict the evolution of the environment, and plan actions under uncertainty.
What Is Deep SSSM?
A Deep Stochastic State-Space Model learns to represent complex, partially observable systems as latent state variables governed by probabilistic transitions:
\[ s_{t+1} \sim p(s_{t+1}\mid s_t, a_t), \quad o_t \sim p(o_t \mid s_t) \]
This latent formulation allows the model to predict how the system evolves while maintaining uncertainty over unobserved variables.
Key Challenges
Real-world robotic environments are often occluded, noisy, or incomplete. Conventional deterministic world models fail in these conditions, producing overconfident predictions that degrade control performance. The challenge is to represent uncertainty explicitly while retaining computational tractability for real-time decision-making.
How Deep SSSM Addresses This
Deep SSSM learns a stochastic latent representation that encodes both dynamics and uncertainty. Instead of predicting single trajectories, it models distributions over future states. This probabilistic approach enables uncertainty-aware control policies that remain stable even under occlusion or unseen disturbances.
Impact and Applications
Deep SSSM provides a foundation for occlusion-robust visual control and uncertainty-aware decision-making in robotics. It has broad implications for:
- Vision-based manipulation under partial observability
- Human–robot collaboration and shared autonomy
- Predictive control under dynamic, uncertain conditions
Toward robots that reason in uncertainty — perceiving, predicting, and acting beyond what is seen.